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Ocean surface winds and currents are coupled through air–sea interaction but are commonly retrieved independently from spaceborne synthetic aperture radar (SAR). For operational single-geometry SAR, simultaneous wind–current retrieval is underdetermined and remains reliant on background information. We propose a physically constrained Bayesian framework that combines radar backscatter and Doppler centroid anomaly measurements to retrieve 10-m wind and radial surface current. An empirical wind-drift relation softly constrains the radial-current increment relative to a product-derived background current, and the solution is obtained by weighted maximum a posteriori estimation. The framework is evaluated using multi-year Sentinel-1 observations collocated with scatterometer, buoy, and high-frequency radar measurements across different sea states. Parameters calibrated in a Gulf Stream-influenced U.S. East Coast region are fixed for temporally held-out events and an independent Southern California test. Across the East Coast data, wind-speed root-mean-square difference (RMSD) decreases by 0.59 − 0.77 m s − 1 relative to ERA5 atmospheric reanalysis. Against high-frequency radar, radial-current RMSD is 0.21 m s − 1 , compared with 0.59 m s − 1 for the operational Sentinel-1 radial-velocity product, 0.48 m s − 1 after a conventional wind-wave Doppler correction, and 0.38 m s − 1 for an ocean-model projection. Event-level ablations show that the physical constraint provides further RMSD reductions of 0.171 m s − 1 and 0.324 m s − 1 in the two independent tests. These results isolate radial-current skill attributable to the physical term beyond joint data fusion alone and demonstrate the value of jointly exploiting complementary SAR observables. The framework provides a practical pathway toward joint kilometer-scale mapping of coastal winds and radial currents from routine single-geometry acquisitions.
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Zhu et al. (2026) studied this question.
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